MONTE CARLO PROJECTIVE CLUSTERING OF TEXTS

Vladimír Ljubopytnov; Jaroslav Pokorný

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Paper

MONTE CARLO PROJECTIVE CLUSTERING OF TEXTS

Topics: Semantic Web Technologies

In Proceedings of the 4th International Conference on Software and Data Technologies - Volume 2: ICSOFT, 237-242, 2009 , Sofia, Bulgaria

Authors: Vladimír Ljubopytnov and Jaroslav Pokorný

Affiliation: Charles University, Czech Republic

Keyword(s): Document clustering, Projective cluster, Web snippets, Web mining, Monte Carlo method.

Related Ontology Subjects/Areas/Topics: Cloud Computing ; Enterprise Information Systems ; Semantic Web Technologies ; Services Science ; Software Agents and Internet Computing

Abstract: In this paper we propose a new, improved version of a Monte Carlo projective clustering algorithm – DOC. DOC was designed for general vector data and we extend it to deal with variable dimension significance and use it in web search snippets clustering. We discuss advantages and weaknesses of our approach with respect to known algorithms.

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Paper citation in several formats:

Ljubopytnov, V. and Pokorný, J. (2009). MONTE CARLO PROJECTIVE CLUSTERING OF TEXTS. In Proceedings of the 4th International Conference on Software and Data Technologies - Volume 2: ICSOFT; ISBN 978-989-674-010-8; ISSN 2184-2833, SciTePress, pages 237-242. DOI: 10.5220/0002247602370242

@conference{icsoft09,
author={Vladimír Ljubopytnov. and Jaroslav Pokorný.},
title={MONTE CARLO PROJECTIVE CLUSTERING OF TEXTS},
booktitle={Proceedings of the 4th International Conference on Software and Data Technologies - Volume 2: ICSOFT},
year={2009},
pages={237-242},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002247602370242},
isbn={978-989-674-010-8},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Software and Data Technologies - Volume 2: ICSOFT
TI - MONTE CARLO PROJECTIVE CLUSTERING OF TEXTS
SN - 978-989-674-010-8
IS - 2184-2833
AU - Ljubopytnov, V.
AU - Pokorný, J.
PY - 2009
SP - 237
EP - 242
DO - 10.5220/0002247602370242
PB - SciTePress